Sulphur 2 distilled — GGUF Q4_K_M for ComfyUI

GGUF Q4_K_M conversion of the distilled diffusion transformer from SulphurAI/Sulphur-2-base, packaged with the BF16 text/audio auxiliary components, the full LTX video VAE, and a validated ComfyUI API workflow.

This package is designed for older NVIDIA GPUs without native NVFP4 tensor cores. On a modified RTX 2080 Ti 22 GB, it was 2.68× faster and used 3.65 GiB less peak VRAM than the existing Sulphur NVFP4 checkpoint in the same controlled workflow.

This is a modified quantized redistribution, not an official SulphurAI or Lightricks release.

Files

GGUF tensor layout: 2,672 F32, 28 BF16, 1,376 Q4_K, and 368 Q6_K tensors. The auxiliary checkpoint contains exactly 4 text-projection, 102 audio-VAE, and 1,227 vocoder tensors, all BF16.

The Gemma-3-12B text encoder is not included. Obtain gemma_3_12B_it_fp4_mixed.safetensors from the upstream LTX/Sulphur text-encoder distribution.

RTX 2080 Ti benchmark

Controlled workflow:

• 832×480
• 73 frames / 3.04 seconds at 24 fps
• 8 distilled steps, Euler, CFG 1.0
• full LTX video VAE
• joint video + audio sampling and AAC output
• same prompt and paired seeds
• reciprocal execution order
• one excluded prewarm per block
• 10 warm samples per format
• isolated ComfyUI GPU

Q4_K_M was 2.683× faster, reduced wall time by 62.7%, and saved 3,742 MiB / 3.65 GiB (17.2%) peak VRAM.

A separate isolated six-second sanity run (832×480, 145 frames, 8 steps, new seed) completed in 96.080 seconds warm and produced a 6.042-second H.264 video with 48 kHz stereo AAC audio. This is a single warm observation, not part of the N=10 benchmark above; the corresponding cold run took 278.714 seconds.

This result is hardware-specific. RTX 2080 Ti is Turing and has no native NVFP4 tensor-core path; the same ranking must not be assumed on Blackwell GPUs such as RTX 5090 or GB10.

Visual and media validation

All 24 benchmark jobs (20 measured + 4 prewarm) produced H.264 MP4s. The validated smoke output was 832×480 at 24 fps with 48 kHz stereo AAC audio.

Paired same-seed frames at 0.5, 1.5, and 2.5 seconds retained coherent subjects, clothing, walking motion, rainy-street reflections, and camera framing. No catastrophic Q4 regression was visible in this sanity check. This is not a formal perceptual-equivalence study; quantized formats can diverge compositionally even with the same seed.

Requirements

• Current ComfyUI with native LTX-2.3 AV nodes
• ComfyUI-GGUF
• Gemma-3-12B text encoder
• NVIDIA GPU with enough combined VRAM/offload capacity

The package was tested with ComfyUI 0.28.0, ComfyUI-GGUF commit 6ea2651, PyTorch 2.6.0+cu124, and --force-fp16 on RTX 2080 Ti.

Installation

Place the files as follows:

ComfyUI/models/diffusion_models/sulphur-distil-ltx23av-Q4_K_M.gguf
ComfyUI/models/checkpoints/sulphur-ltx23av-aux-bf16.safetensors
ComfyUI/models/vae/ltx_full_vae.safetensors
ComfyUI/models/text_encoders/split_files/text_encoders/gemma_3_12B_it_fp4_mixed.safetensors

Load sulphur_t2v_q4km_api.json through the ComfyUI API or use it as a reference when building a UI workflow.

The key graph split is:

• UnetLoaderGGUF loads the Q4_K_M diffusion transformer;
• LTXAVTextEncoderLoader loads Gemma plus sulphur-ltx23av-aux-bf16.safetensors;
• LTXVAudioVAELoader loads audio VAE/vocoder from the same auxiliary checkpoint;
• VAELoader loads ltx_full_vae.safetensors for video decode.

Distilled settings

• Steps: 8
• CFG: 1.0
• Sampler: Euler
• Scheduler: LTXVScheduler (max_shift 2.05, base_shift 0.95, terminal 0.1, stretch enabled)
• Tested resolution: 832×480 at 24 fps

Frame math:

• video frames: (N × 8) + 1
• audio latent frames: video_frames × 4 − 3
• 73 video frames → 289 audio frames → 3.04-second output at 24 fps

Honest caveats

1. The GGUF contains only the diffusion transformer. Keep the auxiliary checkpoint and full VAE installed; otherwise text projection, audio generation, or video decode will be missing.
2. The text encoder is still large. Q4_K_M makes the DiT fit comfortably, but Gemma-3-12B and decoder components still contribute to total VRAM/RAM use.
3. Exact quality equivalence is not claimed. The paired visual gate found no catastrophic regression, but broader prompts, image-to-video, faces, hands, text rendering, and long clips need wider evaluation.
4. Turing results do not predict Blackwell. NVFP4 can be preferable on hardware with native FP4 support.

Conversion notes

The original single-file BF16 checkpoint combines the diffusion transformer, text projection, video/audio VAEs, and vocoder. Only the 4,444-tensor diffusion transformer was converted to GGUF; non-DiT components were not blindly quantized.

The GGUF preserves the upstream config, model_version, and license metadata. scale_shift_table and audio/video learnable_registers remain F32 to preserve loader-visible shapes.

Credits

• Base model: SulphurAI/Sulphur-2-base
• Architecture and license: Lightricks LTX-2
• GGUF runtime: city96/ComfyUI-GGUF
• Quantization, auxiliary packaging, workflow, and RTX 2080 Ti validation: coolthor

License

Distributed under the LTX-2 Community License Agreement dated January 5, 2026, inherited from the upstream model. A complete copy is included as LICENSE and embedded in the GGUF/auxiliary metadata.

The files in this repository were modified through transformer extraction, quantization, and auxiliary-component repackaging. Downstream users must follow the complete Agreement and Attachment A, including its use restrictions and commercial-license threshold.

☕ If this saved you GPU hours, you can buy me a coffee.